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Volume5 ,March 2018.

Volume5,March 2018,

Topic : Analysis of Density-Based Spatial Clustering In
Data Mining

Authors:C.Vinothini || Dr V.Lakshmi Praba

Abstract:Data mining involves the association rule learning, classification, summarization, regression, anomaly detection and
clustering. Clustering is a data mining technique to group the related data into a cluster and unrelated data into different clusters.
Based on the recently described cluster models, there are a lot of clustering that can be applied to a data set in order to partitionate
the information. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is the most well-known density-based
clustering algorithm. The aim is to identify dense regions, which can be measured by the number of objects nearest to a given
point. Unlike K-Means, DBSCAN does not require the number of clusters as a parameter. It infers the number of clusters on its
data, and it can detect clusters of arbitrary shape. Density-based clustering algorithms try to find clusters based on the density of
data points in a region. For the experimental work, we have used the milk data set. The results were analyzed and practically tested
under MATLAB tools.